# attach packages
library(sf)
library(tmap)
library(mapview)
library(tidyverse)
library(here)
library(janitor)
library(paletteer)

sf & tmap

cougars <- sf::read_sf(dsn = here("data", "cougar_connections"), layer = "ds1014") %>% 
  clean_names()
large_corridors <- cougars %>% 
  filter(area_ac > 2000) %>% 
  select(area_ac) # geometry will stick with the data

cougar_sub <- cougars %>% 
  select(elev_mean)
ggplot(data = cougar_sub) +
  geom_sf(aes(fill = elev_mean), color = NA) +
  theme_minimal()

# set to stationary (non-interactive) mode
tmap_mode("plot")  

tm_shape(cougar_sub) +
  tm_fill("elev_mean")

tmap_mode("view") # set to interactive viewing mode

tm_shape(cougar_sub) +
  tm_fill("elev_mean") +
  tm_basemap("Stamen.Terrain")

http://leaflet-extras.github.io/leaflet-providers/preview for a preview of basemaps I can add with tm_basemap!

Some CA maps

### Read in CA eco-regions data
ca_eco <- read_sf(dsn = here("data", "ca_eco_region"), layer = "ca_eco") %>% 
  select(US_L3NAME) %>% 
  rename(region = US_L3NAME) %>% 
  st_simplify(dTolerance = 100) %>% # simplify the number of nodes of a polygon
  st_transform(crs = 4326) # use st_transform when there is crs assigned, use st_crs when there is no crs

# rmapshaper::ms_simplify(), geos_gSimplify()

plot(ca_eco)

### Read in CA Counties polygons
ca_counties <- read_sf(dsn = here("data", "ca_eco_region"), layer = "california_county_shape_file")

st_crs(ca_counties) = 4326
### Read in CA dam
ca_dams <- read_sf(dsn = here("data", "ca_eco_region"), layer = "California_Jurisdictional_Dams") # already set crs to 4326
### Make a map of dams in California, overlayed on eco-regions, with outlines of CA counties
ggplot(data = ca_counties) +
  geom_sf(color = "black",
          size = 0.1) +
  geom_sf(data = ca_eco, 
          aes(fill = region),
          alpha = 0.4,
          color = "NA",
          show.legend = FALSE) +
  geom_sf(data = ca_dams,
          size = 0.5, 
          alpha = 0.4) +
  theme_bw()

### Explore SB county eco-regions
sb <- ca_counties %>% 
  filter(NAME == "Santa Barbara")

# clip eco-region data to only include infotmation within SB county, use st_intersection

eco_clip <- st_intersection(ca_eco, sb)

# Now let's plot it!

ggplot() +
  geom_sf(data = ca_counties, 
          fill = "gray90", 
          color = "gray80",
          size = 0.2) +
  geom_sf(data = eco_clip, 
          aes(fill = region),
          alpha = 0.3,
          color = "white",
          size = 0.4,
          show.legend = FALSE) +
  coord_sf(xlim = c(-121, -119),
           ylim = c(33.5, 35.5)) +
  scale_fill_manual(values = c("magenta", "gold2", "blue")) +
  theme_bw()

### Create interactive map with `tmap`
sb_clip_tmap <- tm_basemap("Esri.WorldImagery") + #?tm_basemap to see link to all options
  tm_shape(eco_clip) +
  tm_fill("region", palette = c("orange", "purple", "green"),
          alpha = 0.5)

tmap_mode("view") # set tmap to interactive viewing mode
sb_clip_tmap

Make a mock dataset using tribble()

### Create an `sf` object from latitude/longitude recording (e.g. in an Excel file)

my_example <- tribble(
  ~id, ~lon, ~lat, #column names
  "tiger", -119.4, 34.35, 
  "lion", -119.41, 34.39,
  "bear", -119.43, 34.38
)

# Convert this to `sf` object:
animals_sf <- st_as_sf(my_example, coords = c("lon", "lat"), # tell longitude and latitude column in order
                       crs = 4326)

Make a map using tmap

animal_map <- tm_basemap("Stamen.Watercolor") +
  tm_shape(animals_sf) +
  tm_dots(labels = "id", 
          col = "purple", 
          size = 0.5)

# tmap set to view mode in previous step

animal_map
### Chloropleth of dam counts
intersection <- st_intersection(x = ca_dams, y = ca_counties)

dams_per_county <- intersection %>% 
  group_by(NAME) %>% 
  tally() # total number of rows in each group

ca_tot <- ca_counties %>% 
  st_join(dams_per_county) %>% 
  select(NAME.x, n) %>% 
  rename(name = NAME.x) 

ca_tot_zero <- ca_tot %>% 
  replace_na(list(n = 0)) # replace na with 0; can replace multiple columns in list()

ggplot() +
  geom_sf(data = ca_tot_zero,
          aes(fill = n),
          alpha = 0.7,
          color = "white",
          size = 0.2) +
  scale_fill_continuous(low = "yellow", high = "red") +
  theme_bw()

Vignettes for sf (google sf package in GitHub) “Geocomputation with R” by Robin Lovelace

Mapview

### Make a world map!

world <- read_sf(dsn = here("data", "TM_WORLD_BORDERS_SIMPL-0.3-1"),
                 layer = "TM_WORLD_BORDERS_SIMPL-0.3") %>% 
  clean_names()

# a quick view with `mapview()`
mapview(world)
world_base <- ggplot(data = world) +
  geom_sf(aes(fill = pop2005),
          color = NA) + # fill by population size in 2005; no country border
  scale_fill_paletteer_c("gameofthrones::margaery")+
  theme_minimal()

world_base

# crop world_base
world_base +
  coord_sf(xlim = c(-20, 50), ylim = c(-40, 40), expand = FALSE)